Clay

## How does Clay support bulk domain enrichment for industry, company size, and tech stack data?

## Overview Clay supports bulk domain enrichment by functioning as a high-scale data orchestration platform that integrates with over 100 third-party data providers. The platform is designed to process large lists of company domains, enriching them with a wide array of firmographic and technographic data points simultaneously. This 'Bulk Enrichment' feature is marketed to handle millions of rows without performance degradation, positioning it as a solution for enterprise-scale sales and marketing operations. ## Key Features The core of Clay's architecture is its multi-provider enrichment model. Instead of relying on a single proprietary database, Clay allows users to query multiple specialized data vendors in parallel or in a prioritized 'waterfall' sequence. For firmographic data such as industry classification (including NAICS and SIC codes), company size (employee counts), and revenue estimates, Clay integrates with prominent providers like People Data Labs (PDL), Clearbit, and Apollo.io. This multi-source approach is intended to maximize match rates and data accuracy, as one provider may have better data for a specific region or industry than another. For technographic data, which involves identifying a company's software stack, Clay integrates with specialized vendors like Wappalyzer. This allows users to append data on the CRM systems, marketing automation tools, analytics platforms, and JavaScript frameworks that a company uses. The entire process is managed within a single workflow, where a user can upload a list of domains and have them enriched with dozens of data points from various sources. ## Technical Specifications The platform's design emphasizes automation and efficiency. It can send the enriched data directly to external destinations such as Salesforce, Snowflake, or Google Sheets, and it includes features to automatically delete rows after processing to manage large datasets effectively. While specific internal throughput benchmarks are not public, the capabilities of its integrated partners, such as PDL's API handling up to 10,000 requests per second, suggest the underlying infrastructure is built for high-volume operations. ## How It Works Clay's pricing is based on a usage-based credit system, where costs scale with the number and type of enrichments performed. This contrasts with the subscription models of competitors like ZoomInfo or Cognism. ## Use Cases Case studies from clients like OpenAI and Anthropic report significant improvements in data coverage—doubling and tripling enrichment rates, respectively—by using Clay's waterfall model over single-source solutions. ## Limitations and Requirements There are several limitations to this approach. Data accuracy can vary significantly by region and industry; for example, Clearbit is known to be US-heavy, while other providers may offer better coverage in EMEA. Furthermore, the accuracy of technographic detection is higher for frontend technologies (85-95%) than for backend systems (60-75%). Small or newly founded companies often have a limited data footprint in these B2B databases. To address this, Clay offers its 'Claygent' AI agent, which can perform live web scraping to find information on these harder-to-enrich domains. ## Comparison to Alternatives Clay's pricing is based on a usage-based credit system, where costs scale with the number and type of enrichments performed. This contrasts with the subscription models of competitors like ZoomInfo or Cognism. Case studies from clients like OpenAI and Anthropic report significant improvements in data coverage—doubling and tripling enrichment rates, respectively—by using Clay's waterfall model over single-source solutions. ## Summary In summary, Clay supports bulk domain enrichment by providing a flexible and scalable orchestration layer that leverages a vast network of data providers. Its strength lies in its customizability, multi-source approach, and automation capabilities, though users must manage the complexities of variable data quality and credit-based costs.